This invention relates to implantable monitors and stimulators generally and more particularly to implantable heart monitors and heart stimulators, such as implantable cardioverter/defibrillators (ICDs).
One of the problems addressed in the design of implantable ICDs is the avoidance of unnecessary electrical shocks delivered to a patient""s heart in response to rapid heart rates caused by exercise (sinus tachycardia) or by atrial fibrillation. Such rhythms are known collectively as supraventricular tachycardias (SVTs). Studies have shown that SVTs may occur in up to 30% of ICD patients. In theory, the shape of the QRS complex in the EGM signal during SVT will not change significantly in most patients, because ventricular depolarizations are caused by normal HIS-Purkinje conduction from the atrium to the ventricle. If high ventricular rates are due to a ventricular tachycardia (VT), one can expect a very different morphology of the electrogram (EGM) signal of the ventricular depolarization (QRS complex) because of a different pattern of electrical activity of the heart during VT. The question thus arises of how to distinguish normal QRS complexes present during SVTs from those indicative of a VT.
One approach to this problem is to study the morphology of the QRS complex and discriminate normal heart beats from abnormal ones based on the similarity of the signal to a sample waveform recorded from the normal heartbeat. The sample waveform is typically referred to as a template. One of the existing methods to discriminate between VT and normal EGM waveforms is based on the properly measured width of the QRS complex. A normal QRS complex is generally narrower than the QRS complex during VT. However there are cases when an abnormal (VT) QRS complex will have a different morphology while remaining narrow. In those cases a more sensitive and selective method is needed to discriminate between different waveforms. The common approach for such morphology analysis is Correlation Waveform Analysis (CWA) or its less computationally costly counterpart, so-called Area of Difference Analysis (AD). Both require minimization of a function describing difference between two signals (sum of squared differences of wave data points for the case of CWA, and the sum of absolute values of the differences for AD). However such computations as typically performed are more computationally costly and require more power than is generally desirable within implantable ICDs.
The present invention comprises a method and apparatus for reliable discrimination between ventricular depolarizations resulting from normal and abnormal propagation of depolarization wavefronts through the chambers of a patient"" heart by means of a wavelet transform based method of analysis of depolarization waveforms. The use of the wavelet transformation based morphology analysis method of the present invention significantly reduces the amount of computation necessary to perform the task. It also performs de-noising of the signal at no additional cost. The present invention may also be used to discriminate between other waveform types, for example, between normal and aberrantly conducted depolarizations of the atrium. The specific embodiments disclosed below, however, are directed toward distinguishing normal and aberrantly conducted ventricular depolarizations.
Three embodiments of wavelet based morphology analysis methods according to the present invention are described in more detail below. A first disclosed embodiment compares template and unknown waveforms in the wavelet domain by ordering wavelet coefficients of the template and unknown waveforms by absolute amplitude and comparing the resulting orders of the coefficients. The second and third disclosed embodiments perform analogs of CWA and AD computations in the wavelet domain. All three methods produce good discrimination of QRS complexes during VTs from normal QRS complexes during SVTs and may be readily implemented in the embedded environments of implantable ICDs. It is believed the embodiments disclosed may also be usefully applied to discriminate between other waveform types, as discussed above.
The wavelet transform is a representation of a signal as a sum of so-called wavelets or little waves. The wavelets are highly localized in time or in the mathematical language, have compact support. The main difference between the wavelet functions used in wavelet transforms and the sine and cosine functions used in the Fourier transform is that wavelets have limited support that scales exponentially. Because of this exponential scaling, wavelet coefficients carry information about time scales present in the signal at various times. Also, wavelets form an orthogonal basis, and in the cases considered in the context of the present invention, these bases are complete, meaning that there are exactly as many wavelets as needed to represent any signal.
There are certain computational advantages of using wavelet transforms instead of Fourier transforms. The wavelet transform will usually yield a small number of coefficients that are adequate to accurately represent the original signal, and thus will achieve a high degree of information compression. This can be especially important for implantable monitors and stimulators because the information compression provided can be employed to substantially reduce the number of required computations. By leaving a small number of wavelet coefficients intact and deleting the rest of them by setting them to zero, the signal can also be efficiently filtered and de-noised.
The gold standard for comparison of waveform morphologies is the correlation waveform analysis (CWA) method, which is based on computation of the correlation function between two waves. However, the computational price of the correlation function is quite high, which makes it undesirable for use in implantable ICDs, which typically employ an 8 or 16 bit CPU running at about 1 MHz clock speed. If one wants the morphology analysis to be independent of the wave amplitude using traditional CWA methodologies, for example, then a 50 sample QRS complex waveform would require normalization at all 50 data points, which would involve 50 integer multiplications and divisions. The traditional correlation function computation will further require calculation of 50 squares and multiple long additions. On the other hand, if one performs this computation in the wavelet domain according to the second and third methods of the present invention, the number of values requiring normalization may be only 10 to 20. Additional reductions in required computations can be obtained by means of a simplified wavelet image comparison methodology according to the first embodiment of the present invention referred to above. Alternative embodiments of the invention apply the so-called Area of Difference approach (AD) or the CWA metric to the selected normalized values derived from the wavelet transform.